Robot Room Cleaner

Updated on 23 June, 2025
Robot Room Cleaner header image

Problem Statement

In a simulation environment, you control a robot located within a grid-represented room. The grid dimensions are m x n, where cells marked as 0 represent barriers and cells marked as 1 indicate open paths that the robot can navigate. The robot's starting position is not pre-known but is assured to be in an open path (1). The task is to program the robot to clean every accessible area of the room without manual intervention.

The Robot can interact with its environment using the following APIs:

  1. move(): Tries to move the robot one cell in its current direction. Returns true if successful (the next cell is open) and moves the robot; returns false if there's a barrier and the robot remains stationary.
  2. turnLeft() and turnRight(): Rotates the robot 90 degrees left or right respectively, without moving from its current spot.
  3. clean(): Commands the robot to clean the cell it's currently occupying.

Complicating the task, you do not have direct access to the grid's data, asking you to make decisions based only on the robot's interactions with the environment. All grid peripheries are walled, ensuring no movement beyond boundaries.

Examples

Example 1

Input:

room = [[1,1,1,1,1,0,1,1],[1,1,1,1,1,0,1,1],[1,0,1,1,1,1,1,1],[0,0,0,1,0,0,0,0],[1,1,1,1,1,1,1,1]], row = 1, col = 3

Output:

Robot cleaned all rooms.

Explanation:

All grids in the room are marked by either 0 or 1.
0 means the cell is blocked, while 1 means the cell is accessible.
The robot initially starts at the position of row=1, col=3.
From the top left corner, its position is one row below and three columns right.

Example 2

Input:

room = [[1]], row = 0, col = 0

Output:

Robot cleaned all rooms.

Constraints

  • m == room.length
  • n == room[i].length
  • 1 <= m <= 100
  • 1 <= n <= 200
  • room[i][j] is either 0 or 1.
  • 0 <= row < m
  • 0 <= col < n
  • room[row][col] == 1
  • All the empty cells can be visited from the starting position.

Approach and Intuition

The task requires robust algorithms for autonomous exploration without any prior knowledge of the environment. The challenge is to ensure comprehensive exploration and cleaning without revisiting cells, efficiently managing operations and handling the robot’s limited view of its surroundings. Here’s how one might approach this:

  1. Depth-First Search (DFS) with Backtracking:

    • Implement DFS to explore every reachable cell, starting from the robot's initial position. Every time the robot moves to a new cell, it marks it as cleaned. If the robot encounters a wall (move() returns false), it backtracks to explore alternative paths.
    • This method ensures that each accessible cell is visited once without redundant operations.
  2. Directional Control and State Tracking:

    • Maintain a directional state for the robot using an array or a similar structure that logs its current facing direction (up, down, left, right). This will help simulate the robot's movements accurately.
    • Using the robot's APIs (turnLeft(), turnRight(), move()), direct the robot according to the DFS strategy, altering directions when a barrier is detected.
  3. Recursive Exploration Detail:

    • When the robot can move forward (open path), call clean() and recursively continue the exploration from the new cell.
    • If blocked by a wall, use turnLeft() and turnRight() to adjust the robot’s orientation, checking neighbouring cells. Utilize condition checks to decide the next action based on previous moves to minimize disorientation and inefficiency.
  4. Handling Boundaries and Obstacles:

    • Given the constraints that all cell edges are surrounded by walls, add bounding checks before making a move decision.
    • Use the sensor feedback (move() API response) strategically to detect and handle surroundings, making this a case of reactive navigation.
  5. Cycle Avoidance & Space Complexity Management:

    • Implement a set or matrix to keep track of visited cells to avoid unnecessary re-cleaning and ensure the robot doesn't enter an infinite loop.
    • Choose data structures wisely considering space complexity, as the robot’s operational environment can be as large as 200 by 100 cells.

This approach combines computational geometry, pathfinding algorithms, and real-time decision-making to solve the problem of cleaning an entire room autonomously, ensuring comprehensive area service with logical movement sequences.

Solutions

  • Java
  • Python
java
class Solution {
    int[][] moves = { { -1, 0 }, { 0, 1 }, { 1, 0 }, { 0, -1 } };
    Set<Pair<Integer, Integer>> seen = new HashSet();
    Robot cleaner;

    public void reverse() {
        cleaner.turnRight();
        cleaner.turnRight();
        cleaner.move();
        cleaner.turnRight();
        cleaner.turnRight();
    }

    public void explore(int x, int y, int dir) {
        seen.add(new Pair(x, y));
        cleaner.clean();
        for (int i = 0; i < 4; ++i) {
            int newDir = (dir + i) % 4;
            int newX = x + moves[newDir][0];
            int newY = y + moves[newDir][1];

            if (!seen.contains(new Pair(newX, newY)) && cleaner.move()) {
                explore(newX, newY, newDir);
                reverse();
            }
            cleaner.turnRight();
        }
    }

    public void cleanRoom(Robot robot) {
        this.cleaner = robot;
        explore(0, 0, 0);
    }
}

For the "Robot Room Cleaner" problem, implement a solution that enables a robotic cleaner to navigate and clean an entire room efficiently. The given Java code uses recursive backtracking to achieve this. Here’s a breakdown of how the solution works:

  • Data Structures and Initialization:

    • An array moves that represents the four possible directions the robot can move: up, right, down, and left.
    • A HashSet named seen to keep track of all the cells that have been visited and cleaned.
    • A Robot interface named cleaner which needs methods like turnRight(), move(), and clean().
  • Utility Functions:

    • reverse() - This method ensures the robot can backtrack to the previous position. It achieves this by turning around 180 degrees, moving one step forward, and then facing back in the original direction.
  • Core Functionality:

    • explore(int x, int y, int dir) - This recursive method is central to the solution. It starts by marking the current position as cleaned. Then, for each of the four possible directions:
      • Calculates the next position based on the current direction.
      • Checks if the new position has not been visited and is accessible.
      • Moves the robot to the new position, cleans it, and recursively explores further from there.
      • On returning from the recursion (post deeper exploration), reverses the robot’s movement to backtrack.
      • Rotates the robot to the next direction to explore.
  • Execution Entry Point:

    • cleanRoom(Robot robot) - This method initializes the robot interface and starts the cleaning process from the initial position (0,0) and facing in the initial direction 0.

Ensure that the robot’s interface is correctly implemented to support operations required by this solution. This approach efficiently ensures all reachable parts of the room are cleaned without re-cleaning any area. The recursive depth-first search combined with backtracking provides thorough coverage while the seen set prevents redundant operations.

python
class Solution:
    def tidyRoom(self, automatedCleaner):
        """
        :type automatedCleaner: Robot
        :rtype: None
        """
        def retreat():
            automatedCleaner.turnRight()
            automatedCleaner.turnRight()
            automatedCleaner.move()
            automatedCleaner.turnRight()
            automatedCleaner.turnRight()

        def explore(cell=(0, 0), direction=0):
            visited.add(cell)
            automatedCleaner.clean()
            for i in range(4):
                new_direction = (direction + i) % 4
                new_position = (cell[0] + movement[new_direction][0],
                                cell[1] + movement[new_direction][1])

                if new_position not in visited and automatedCleaner.move():
                    explore(new_position, new_direction)
                    retreat()
                automatedCleaner.turnRight()

        movement = [(-1, 0), (0, 1), (1, 0), (0, -1)]
        visited = set()
        explore()

The "Robot Room Cleaner" solution in Python involves a robot that cleans a room autonomously by navigating through all accessible areas. Here's a concise summary of how the implementation works using Python classes and functions:

  • A Solution class contains the method tidyRoom that receives an instance of Robot as its parameter.
  • The inner function retreat() handles the robot's movement to backtrack. It involves turning the robot around (180 degrees) by executing two right turns, moving one step backward, and then aligning back to the original facing direction with two additional right turns.
  • The explore function is a recursive method that takes two parameters: cell, indicating the robot's current position with a default starting position of (0, 0), and direction, representing the current facing direction with a default direction of 0.
  • In the explore function:
    • The robot's current cell is added to the visited set.
    • The clean() method of the robot is called to clean the current cell.
    • The robot attempts to explore in all four directions (represented by the indices 0-3). The direction is dynamically adjusted based on the robot's current direction plus the incremental directions from a loop.
    • A new position is calculated based on the current direction. If this new position has not been visited and the robot can move there (automatedCleaner.move() returns True), the explore function is recursively called.
    • After exploring the new position, the robot then calls the retreat() function to go back to the previous cell before continuing to turn and explore other directions.
  • The movement list defines the change in coordinates corresponding to each direction (up, right, down, left).
  • The visited set keeps track of all cells that the robot has cleaned to prevent redundant cleaning and movement.

Effectively implement this recursive backtracking approach to ensure the robot cleans the entire accessible area without repeating any locations. By combining the recursive exploration of areas and manual management of robot orientation and position, this solution ensures thorough cleaning of the room.

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